In parallel, promising recent work in deep learning based image reconstruction has used generative modeling to learn image priors separate from the measurement forward model [1]}, [2]}. By explicitly decoupling the forward model and the image prior, generative model based techniques have an increased robustness to test time shifts in the forward operator such as sampling trajectory [2]}. As a result, the reconstruction can flexibly handle other modifications to the forward model without retraining the generative prior.
